Preprocessing plays a crucial role in the face recognition process as it can affect the performance of feature extraction networks [1]}.
For all datasets used in this work – except for IJB-C – hand-labeled facial landmarks are available, which can be used to align faces directly according to the desired size and the location of these landmarks in the target images.
Unfortunately, many research papers lack detailed information on how preprocessing is done, making it particularly difficult for others to reproduce experiments.
For ArcFace, only the required input image dimension of \(112\times 112\)  is provided [2]}.
There were also some scripts to align faces based on landmarks detected with a MTCNN [3]}, but our hand-labeled landmarks do not correspond to those extracted by MTCNN and, thus, it was not entirely clear how to achieve alignment.
Six pictures, which indicate some kind of alignment, could be taken from their GitHubhttps://github.com/deepinsight/insightface repository, which we used to manually estimate average landmark locations in the target images.
In a similar manner, face images are cropped to \(160\times 160\)  for Facenet.
